303 research outputs found

    Data- og ekspertdreven variabelseleksjon for prediktive modeller i helsevesenet : mot økt tolkbarhet i underbestemte maskinlæringsproblemer

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    Modern data acquisition techniques in healthcare generate large collections of data from multiple sources, such as novel diagnosis and treatment methodologies. Some concrete examples are electronic healthcare record systems, genomics, and medical images. This leads to situations with often unstructured, high-dimensional heterogeneous patient cohort data where classical statistical methods may not be sufficient for optimal utilization of the data and informed decision-making. Instead, investigating such data structures with modern machine learning techniques promises to improve the understanding of patient health issues and may provide a better platform for informed decision-making by clinicians. Key requirements for this purpose include (a) sufficiently accurate predictions and (b) model interpretability. Achieving both aspects in parallel is difficult, particularly for datasets with few patients, which are common in the healthcare domain. In such cases, machine learning models encounter mathematically underdetermined systems and may overfit easily on the training data. An important approach to overcome this issue is feature selection, i.e., determining a subset of informative features from the original set of features with respect to the target variable. While potentially raising the predictive performance, feature selection fosters model interpretability by identifying a low number of relevant model parameters to better understand the underlying biological processes that lead to health issues. Interpretability requires that feature selection is stable, i.e., small changes in the dataset do not lead to changes in the selected feature set. A concept to address instability is ensemble feature selection, i.e. the process of repeating the feature selection multiple times on subsets of samples of the original dataset and aggregating results in a meta-model. This thesis presents two approaches for ensemble feature selection, which are tailored towards high-dimensional data in healthcare: the Repeated Elastic Net Technique for feature selection (RENT) and the User-Guided Bayesian Framework for feature selection (UBayFS). While RENT is purely data-driven and builds upon elastic net regularized models, UBayFS is a general framework for ensembles with the capabilities to include expert knowledge in the feature selection process via prior weights and side constraints. A case study modeling the overall survival of cancer patients compares these novel feature selectors and demonstrates their potential in clinical practice. Beyond the selection of single features, UBayFS also allows for selecting whole feature groups (feature blocks) that were acquired from multiple data sources, as those mentioned above. Importance quantification of such feature blocks plays a key role in tracing information about the target variable back to the acquisition modalities. Such information on feature block importance may lead to positive effects on the use of human, technical, and financial resources if systematically integrated into the planning of patient treatment by excluding the acquisition of non-informative features. Since a generalization of feature importance measures to block importance is not trivial, this thesis also investigates and compares approaches for feature block importance rankings. This thesis demonstrates that high-dimensional datasets from multiple data sources in the medical domain can be successfully tackled by the presented approaches for feature selection. Experimental evaluations demonstrate favorable properties of both predictive performance, stability, as well as interpretability of results, which carries a high potential for better data-driven decision support in clinical practice.Moderne datainnsamlingsteknikker i helsevesenet genererer store datamengder fra flere kilder, som for eksempel nye diagnose- og behandlingsmetoder. Noen konkrete eksempler er elektroniske helsejournalsystemer, genomikk og medisinske bilder. Slike pasientkohortdata er ofte ustrukturerte, høydimensjonale og heterogene og hvor klassiske statistiske metoder ikke er tilstrekkelige for optimal utnyttelse av dataene og god informasjonsbasert beslutningstaking. Derfor kan det være lovende å analysere slike datastrukturer ved bruk av moderne maskinlæringsteknikker for å øke forståelsen av pasientenes helseproblemer og for å gi klinikerne en bedre plattform for informasjonsbasert beslutningstaking. Sentrale krav til dette formålet inkluderer (a) tilstrekkelig nøyaktige prediksjoner og (b) modelltolkbarhet. Å oppnå begge aspektene samtidig er vanskelig, spesielt for datasett med få pasienter, noe som er vanlig for data i helsevesenet. I slike tilfeller må maskinlæringsmodeller håndtere matematisk underbestemte systemer og dette kan lett føre til at modellene overtilpasses treningsdataene. Variabelseleksjon er en viktig tilnærming for å håndtere dette ved å identifisere en undergruppe av informative variabler med hensyn til responsvariablen. Samtidig som variabelseleksjonsmetoder kan lede til økt prediktiv ytelse, fremmes modelltolkbarhet ved å identifisere et lavt antall relevante modellparametere. Dette kan gi bedre forståelse av de underliggende biologiske prosessene som fører til helseproblemer. Tolkbarhet krever at variabelseleksjonen er stabil, dvs. at små endringer i datasettet ikke fører til endringer i hvilke variabler som velges. Et konsept for å adressere ustabilitet er ensemblevariableseleksjon, dvs. prosessen med å gjenta variabelseleksjon flere ganger på en delmengde av prøvene i det originale datasett og aggregere resultater i en metamodell. Denne avhandlingen presenterer to tilnærminger for ensemblevariabelseleksjon, som er skreddersydd for høydimensjonale data i helsevesenet: "Repeated Elastic Net Technique for feature selection" (RENT) og "User-Guided Bayesian Framework for feature selection" (UBayFS). Mens RENT er datadrevet og bygger på elastic net-regulariserte modeller, er UBayFS et generelt rammeverk for ensembler som muliggjør inkludering av ekspertkunnskap i variabelseleksjonsprosessen gjennom forhåndsbestemte vekter og sidebegrensninger. En case-studie som modellerer overlevelsen av kreftpasienter sammenligner disse nye variabelseleksjonsmetodene og demonstrerer deres potensiale i klinisk praksis. Utover valg av enkelte variabler gjør UBayFS det også mulig å velge blokker eller grupper av variabler som representerer de ulike datakildene som ble nevnt over. Kvantifisering av viktigheten av variabelgrupper spiller en nøkkelrolle for forståelsen av hvorvidt datakildene er viktige for responsvariablen. Tilgang til slik informasjon kan føre til at bruken av menneskelige, tekniske og økonomiske ressurser kan forbedres dersom informasjonen integreres systematisk i planleggingen av pasientbehandlingen. Slik kan man redusere innsamling av ikke-informative variabler. Siden generaliseringen av viktighet av variabelgrupper ikke er triviell, undersøkes og sammenlignes også tilnærminger for rangering av viktigheten til disse variabelgruppene. Denne avhandlingen viser at høydimensjonale datasett fra flere datakilder fra det medisinske domenet effektivt kan håndteres ved bruk av variabelseleksjonmetodene som er presentert i avhandlingen. Eksperimentene viser at disse kan ha positiv en effekt på både prediktiv ytelse, stabilitet og tolkbarhet av resultatene. Bruken av disse variabelseleksjonsmetodene bærer et stort potensiale for bedre datadrevet beslutningsstøtte i klinisk praksis

    Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

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    The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligence (AI) and machine learning (ML) have emerged as the most promising approaches to automate the CHA process. In this paper, we explore the background of CHA and delve into the extensive research recently undertaken in this domain to provide a comprehensive survey of the state-of-the-art. In particular, a careful selection of significant works published in the literature is reviewed to elaborate a range of enabling technologies and AI/ML techniques used for CHA, including conventional supervised and unsupervised machine learning, deep learning, reinforcement learning, natural language processing, and image processing techniques. Furthermore, we provide an overview of various means of data acquisition and the benchmark datasets. Finally, we discuss open issues and challenges in using AI and ML for CHA along with some possible solutions. In summary, this paper presents CHA tools, lists various data acquisition methods for CHA, provides technological advancements, presents the usage of AI for CHA, and open issues, challenges in the CHA domain. We hope this first-of-its-kind survey paper will significantly contribute to identifying research gaps in the complex and rapidly evolving interdisciplinary mental health field

    Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review

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    Rational: Deep learning (DL) has demonstrated a remarkable performance in diagnostic imaging for various diseases and modalities and therefore has a high potential to be used as a clinical tool. However, current practice shows low deployment of these algorithms in clinical practice, because DL algorithms lack transparency and trust due to their underlying black-box mechanism. For successful employment, explainable artificial intelligence (XAI) could be introduced to close the gap between the medical professionals and the DL algorithms. In this literature review, XAI methods available for magnetic resonance (MR), computed tomography (CT), and positron emission tomography (PET) imaging are discussed and future suggestions are made.Methods: PubMed, and Clarivate Analytics/Web of Science Core Collection were screened. Articles were considered eligible for inclusion if XAI was used (and well described) to describe the behavior of a DL model used in MR, CT and PET imaging.Results: A total of 75 articles were included of which 54 and 17 articles described post and ad hoc XAI methods, respectively, and 4 articles described both XAI methods. Major variations in performance is seen between the methods. Overall, post hoc XAI lacks the ability to provide class-discriminative and target-specific explanation. Ad hoc XAI seems to tackle this because of its intrinsic ability to explain. However, quality control of the XAI methods is rarely applied and therefore systematic comparison between the methods is difficult.Conclusion: There is currently no clear consensus on how XAI should be deployed in order to close the gap between medical professionals and DL algorithms for clinical implementation. We advocate for systematic technical and clinical quality assessment of XAI methods. Also, to ensure end-to-end unbiased and safe integration of XAI in clinical workflow, (anatomical) data minimization and quality control methods should be included

    Audiovisual speech perception in cochlear implant patients

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    Hearing with a cochlear implant (CI) is very different compared to a normal-hearing (NH) experience, as the CI can only provide limited auditory input. Nevertheless, the central auditory system is capable of learning how to interpret such limited auditory input such that it can extract meaningful information within a few months after implant switch-on. The capacity of the auditory cortex to adapt to new auditory stimuli is an example of intra-modal plasticity — changes within a sensory cortical region as a result of altered statistics of the respective sensory input. However, hearing deprivation before implantation and restoration of hearing capacities after implantation can also induce cross-modal plasticity — changes within a sensory cortical region as a result of altered statistics of a different sensory input. Thereby, a preserved cortical region can, for example, support a deprived cortical region, as in the case of CI users which have been shown to exhibit cross-modal visual-cortex activation for purely auditory stimuli. Before implantation, during the period of hearing deprivation, CI users typically rely on additional visual cues like lip-movements for understanding speech. Therefore, it has been suggested that CI users show a pronounced binding of the auditory and visual systems, which may allow them to integrate auditory and visual speech information more efficiently. The projects included in this thesis investigate auditory, and particularly audiovisual speech processing in CI users. Four event-related potential (ERP) studies approach the matter from different perspectives, each with a distinct focus. The first project investigates how audiovisually presented syllables are processed by CI users with bilateral hearing loss compared to NH controls. Previous ERP studies employing non-linguistic stimuli and studies using different neuroimaging techniques found distinct audiovisual interactions in CI users. However, the precise timecourse of cross-modal visual-cortex recruitment and enhanced audiovisual interaction for speech related stimuli is unknown. With our ERP study we fill this gap, and we present differences in the timecourse of audiovisual interactions as well as in cortical source configurations between CI users and NH controls. The second study focuses on auditory processing in single-sided deaf (SSD) CI users. SSD CI patients experience a maximally asymmetric hearing condition, as they have a CI on one ear and a contralateral NH ear. Despite the intact ear, several behavioural studies have demonstrated a variety of beneficial effects of restoring binaural hearing, but there are only few ERP studies which investigate auditory processing in SSD CI users. Our study investigates whether the side of implantation affects auditory processing and whether auditory processing via the NH ear of SSD CI users works similarly as in NH controls. Given the distinct hearing conditions of SSD CI users, the question arises whether there are any quantifiable differences between CI user with unilateral hearing loss and bilateral hearing loss. In general, ERP studies on SSD CI users are rather scarce, and there is no study on audiovisual processing in particular. Furthermore, there are no reports on lip-reading abilities of SSD CI users. To this end, in the third project we extend the first study by including SSD CI users as a third experimental group. The study discusses both differences and similarities between CI users with bilateral hearing loss and CI users with unilateral hearing loss as well as NH controls and provides — for the first time — insights into audiovisual interactions in SSD CI users. The fourth project investigates the influence of background noise on audiovisual interactions in CI users and whether a noise-reduction algorithm can modulate these interactions. It is known that in environments with competing background noise listeners generally rely more strongly on visual cues for understanding speech and that such situations are particularly difficult for CI users. As shown in previous auditory behavioural studies, the recently introduced noise-reduction algorithm "ForwardFocus" can be a useful aid in such cases. However, the questions whether employing the algorithm is beneficial in audiovisual conditions as well and whether using the algorithm has a measurable effect on cortical processing have not been investigated yet. In this ERP study, we address these questions with an auditory and audiovisual syllable discrimination task. Taken together, the projects included in this thesis contribute to a better understanding of auditory and especially audiovisual speech processing in CI users, revealing distinct processing strategies employed to overcome the limited input provided by a CI. The results have clinical implications, as they suggest that clinical hearing assessments, which are currently purely auditory, should be extended to audiovisual assessments. Furthermore, they imply that rehabilitation including audiovisual training methods may be beneficial for all CI user groups for quickly achieving the most effective CI implantation outcome

    Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions

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    Parkinson’s disease (PD) is a progressive and complex neurodegenerative disorder associated with age that affects motor and cognitive functions. As there is currently no cure, early diagnosis and accurate prognosis are essential to increase the effectiveness of treatment and control its symptoms. Medical imaging, specifically magnetic resonance imaging (MRI), has emerged as a valuable tool for developing support systems to assist in diagnosis and prognosis. The current literature aims to improve understanding of the disease’s structural and functional manifestations in the brain. By applying artificial intelligence to neuroimaging, such as deep learning (DL) and other machine learning (ML) techniques, previously unknown relationships and patterns can be revealed in this high-dimensional data. However, several issues must be addressed before these solutions can be safely integrated into clinical practice. This review provides a comprehensive overview of recent ML techniques analyzed for the automatic diagnosis and prognosis of PD in brain MRI. The main challenges in applying ML to medical diagnosis and its implications for PD are also addressed, including current limitations for safe translation into hospitals. These challenges are analyzed at three levels: disease-specific, task- specific, and technology-specific. Finally, potential future directions for each challenge and future perspectives are discusse

    Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

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    Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

    Advancing clinical evaluation and diagnostics with artificial intelligence technologies

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    Machine Learning (ML) is extensively used in diverse healthcare applications to aid physicians in diagnosing and identifying associations, sometimes hidden, between dif- ferent biomedical parameters. This PhD thesis investigates the interplay of medical images and biosignals to study the mechanisms of aging, knee cartilage degeneration, and Motion Sickness (MS). The first study shows the predictive power of soft tissue radiodensitometric parameters from mid-thigh CT scans. We used data from the AGES-Reykjavik study, correlating soft tissue numerical profiles from 3,000 subjects with cardiac pathophysiologies, hy- pertension, and diabetes. The results show the role of fat, muscle, and connective tissue in the evaluation of healthy aging. Moreover, we classify patients experiencing gait symptoms, neurological deficits, and a history of stroke in a Korean population, reveal- ing the significant impact of cognitive dual-gait analysis when coupled with single-gait. The second study establishes new paradigms for knee cartilage assessment, correlating 2D and 3D medical image features obtained from CT and MRI scans. In the frame of the EU-project RESTORE we were able to classify degenerative, traumatic, and healthy cartilages based on their bone and cartilage features, as well as we determine the basis for the development of a patient-specific cartilage profile. Finally, in the MS study, based on a virtual reality simulation synchronized with a moving platform and EEG, heart rate, and EMG, we extracted over 3,000 features and analyzed their importance in predicting MS symptoms, concussion in female ath- letes, and lifestyle influence. The MS features are extracted from the brain, muscle, heart, and from the movement of the center of pressure during the experiment and demonstrate their potential value to advance quantitative evaluation of postural con- trol response. This work demonstrates, through various studies, the importance of ML technologies in improving clinical evaluation and diagnosis contributing to advance our understanding of the mechanisms associated with pathological conditions.Tölvulærdómur (Machine Learning eða ML) er algjörlega viðurkennt og nýtt í ýmsum heilbrigðisþjónustuviðskiptum til að hjálpa læknunum við að greina og finna tengsl milli mismunandi líffærafræðilegra gilda, stundum dulinna. Þessi doktorsritgerð fjallar um samspil læknisfræðilegra mynda og lífsmerkja til að skoða eðli aldrunar, niðurbrot hnéhringjar og hreyfikerfissjúkdóms (Motion Sickness eða MS). Fyrsta rannsóknin sýnir spárkraft midjubeins-CT-skanna í því að fullyrða staðfest- ar meðalþyngdarlíkön, þar sem gögn úr AGES-Reykjavik-rannsókninni eru tengd við hjarta- og æðafræðilega sjúkdóma, blóðþrýstingsveikindi og sykursýki hjá 3.000 þátt- takendum. Niðurstöðurnar sýna hlutverk fitu, vöðva og tengikjarna í mati á heilbrigð- um öldrun. Þar að auki flokkum við sjúklinga sem upplifa gangvandamál, taugaein- kenni og sögu af heilablóðfalli í kóreanskri þjóð, þar sem einstök gangtaksskoðun er tengd saman við tvískoðun. Önnur rannsóknin setur upp ný tölfræðisfræðileg umhverfisviðmið til matar á hnéhringju með samhengi 2D og 3D mynda sem aflað er úr CT og MRI-skömmtum. Í rauninni höfum við getuð flokkað niðurbrots-, slys- og heilbrigðar hnéhringjur á grundvelli bein- og brjóskmerkja með raun að sækja niðurstöður í umfjöllun um sjúklingar eftir réttu einkasniði. Að lokum, í MS-rannsókninni, notum við myndræn tilraun samþættaða með hreyfan- legan grundvöll og EEG, hjartslátt, EMG þar sem yfir 3.000 aðgerðir eru útfránn og greindir til að átta sig á áhrifum MS, höfuðárás hjá konum sem eru íþróttamenn, lífs- stíl og fleira. Einkenni MS eru aflöguð úr heilanum, vöðvum, hjarta og frá hreyfingum þyngdupunktsins á meðan tilraunin stendur og sýna mög

    Disease progression and genetic risk factors in the primary tauopathies

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    The primary tauopathies are a group of progressive neurodegenerative diseases within the frontotemporal lobar degeneration spectrum (FTLD) characterised by the accumulation of misfolded, hyperphosphorylated microtubule-associated tau protein (MAPT) within neurons and glial cells. They can be classified according to the underlying ratio of three-repeat (3R) to four-repeat (4R) tau and include Pick’s disease (PiD), which is the only 3R tauopathy, and the 4R tauopathies the most common of which are progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). There are no disease modifying therapies currently available, with research complicated by the wide variability in clinical presentations for each underlying pathology, with presentations often overlapping, as well as the frequent occurrence of atypical presentations that may mimic other non-FTLD pathologies. Although progress has been made in understanding the genetic contribution to disease risk in the more common 4R tauopathies (PSP and CBD), very little is known about the genetics of the 3R tauopathy PiD. There are two broad aims to this thesis; firstly, to use data-driven generative models of disease progression to try and more accurately stage and subtype patients presenting with PSP and corticobasal syndrome (CBS, the most common presentation of CBD), and secondly to identify genetic drivers of disease risk and progression in PiD. Given the rarity of these disorders, as part of this PhD I had to assemble two large cohorts through international collaboration, the 4R tau imaging cohort and the Pick’s disease International Consortium (PIC), to build large enough sample sizes to enable the required analyses. In Chapter 3 I use a probabilistic event-based modelling (EBM) approach applied to structural MRI data to determine the sequence of brain atrophy changes in clinically diagnosed PSP - Richardson syndrome (PSP-RS). The sequence of atrophy predicted by the model broadly mirrors the sequential spread of tau pathology in PSP post-mortem staging studies, and has potential utility to stratify PSP patients on entry into clinical trials based on disease stage, as well as track disease progression. To better characterise the spatiotemporal heterogeneity of the 4R tauopathies, I go on to use Subtype and Stage Inference (SuStaIn), an unsupervised machine algorithm, to identify population subgroups with distinct patterns of atrophy in PSP (Chapter 4) and CBS (Chapter 5). The SuStaIn model provides data-driven evidence for the existence of two spatiotemporal subtypes of atrophy in clinically diagnosed PSP, giving insights into the relationship between pathology and clinical syndrome. In CBS I identify two distinct imaging subtypes that are differentially associated with underlying pathology, and potentially a third subtype that if confirmed in a larger dataset may allow the differentiation of CBD from both PSP and AD pathology using a baseline MRI scan. In Chapter 6 I investigate the association between the MAPT H1/H2 haplotype and PiD, showing for the first time that the H2 haplotype, known to be strongly protective against developing PSP or CBD, is associated with an increased risk of PiD. This is an important finding and has implications for the future development of MAPT isoform-specific therapeutic strategies for the primary tauopathies. In Chapter 7 I perform the first genome wide association study (GWAS) in PiD, identifying five genomic loci that are nominally associated with risk of disease. The top two loci implicate perturbed GABAergic signalling (KCTD8) and dysregulation of the ubiquitin proteosome system (TRIM22) in the pathogenesis of PiD. In the final chapter (Chapter 8) I investigate the genetic determinants of survival in PiD, by carrying out a Cox proportional hazards genome wide survival study (GWSS). I identify a genome-wide significant association with survival on chromosome 3, within the NLGN1 gene. which encodes a synaptic scaffolding protein located at the neuronal pre-synaptic membrane. Loss of synaptic integrity with resulting dysregulation of synaptic transmission leading to increased pathological tau accumulation is a plausible mechanism though which NLGN1 dysfunction could impact on survival in PiD

    Novel digital biomarkers for frontotemporal dementia

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    Frontotemporal dementia (FTD) is a heterogenous neurodegenerative disease and is caused by an autosomal dominant mutation in around one third of cases. This pattern of inheritance enables FTD to be studied in the presymptomatic phase, where individuals carry the genetic mutation but have yet to develop symptoms. There are currently no approved treatments for FTD, although clinical trials aiming to target interventions at the earliest disease stage, are underway. There is an urgent need for biomarkers that can reliably detect and monitor the progression of disease in the presymptomatic period, though there are a distinct lack of sensitive cognitive measures. This thesis aims to establish the validity and sensitivity of a set of digital biomarkers that can be used to measure cognitive function in FTD. I begin this thesis by describing the Ignite computerised cognitive assessment, developing normative properties for the tests through a remote data collection study in over 2,000 healthy controls. I build upon this validation by establishing the concurrent validity of Ignite with gold-standard pen and paper tasks, the test-retest reliability upon repeated administration, and demonstrate the tests are sensitive to presymptomatic impairment across several cognitive domains. I also describe a novel portable eye tracking experiment that can be completed outside of the lab, first highlighting the validity of the tests as measures of cognitive function and demonstrating their sensitivity in detecting early changes in social cognition in the presymptomatic period. Finally, I investigate a smartphone app that passively monitors human-device interactions to generate digital biomarkers of cognitive function. I establish the acceptability of the app in the general population before demonstrating the measures produced can detect differences in keyboard interactions in presymptomatic FTD mutation carriers. This work provides evidence that biomarkers generated from different digital devices are valid and sensitive measures of cognitive impairment in FTD. Therefore, digital biomarkers could replace outdated pen and paper tasks and be used as outcome measures in clinical trials

    Improving the evaluation and treatment of neuroendocrine disorders

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    The aim of this thesis was to improve the diagnosis and treatment of neuroendocrine disorders. In addition, it shows further possibilities to achieve this goal in the long term
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